MétaCan
Menu
Back to cohort
Record W7093319103 · doi:10.23977/aetp.2025.090518

Dual-Dimensional Collaborative Teaching Reform of Single-Chip Microcomputer Principles Course for Technology Empowerment and Value Leadership

2025· article· W7093319103 on OpenAlexvenueno aff

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2025
Typearticle
Language
FieldSocial Sciences
TopicEducation and Vocational Training
Canadian institutionsnot available
FundersUniversity of Shanghai for Science and Technology
KeywordsContext (archaeology)EmpowermentIdeologyCourse (navigation)ComprehensionMicrocomputerValue (mathematics)

Abstract

fetched live from OpenAlex

This study focuses on the Single-Chip Microcomputer Principles course for Electrical Engineering and Automation. The objective is to explore effective methodologies for the organic integration of technical education with ideological and political education. In the context of teaching practice, the deep integration of technology and ideological and political education is advanced from the key dimensions of the application of domestic chips and the cultivation of craftsmanship spirit. The teaching effectiveness was evaluated through a series of activities, including prominent competitions. To date, students have been awarded over 100 prizes at the provincial level and above. The research findings indicate that the practice scenarios developed by pocket labs have led to a substantial enhancement in students' awareness of technology ethics. Furthermore, the study has revealed that students have acquired a more profound comprehension of the ethical responsibilities and values associated with technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.426
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Educational Technology and PsychologySame topicEducation and Vocational TrainingFrench-language works237,207